The technology disclosed relates to testing hybrid positioning systems, including systems that rely on MEMS sensors. In particular, it relates to methods and devices for configuring and running tests of hybrid positioning systems.
Designers of smart phones, hand-held devices and other portable devices that deliver position data face increasing challenges to deliver accurate location data in difficult environments. The challenges arise from both regulatory requirements and consumer expectations. At least in the United States, regulations for E-911 service require manufacturers of some communication devices that are used to contact 911 emergency services to assist emergency operators in locating the caller. This is evolving to include identification of the floor in a building from which the call originated. From consumer's expectations, requirements for accurate positioning are more exacting. Consumers reportedly desire information for navigation purposes with an accuracy of 3 to 10 m, in all environments. Challenging environments include urban canyons, where multi-path reflections of signals complicate analysis. Even more challenging are indoor environments that either receive no signal from global navigation satellite systems (GNSS) or that receive only reflected, distorted and/or weak signals.
Space ships, aircraft and rockets have used expensive inertial guidance systems as an alternative to relying on global navigation satellite constellations. Inertial guidance systems support dead reckoning, both intermittently and for extended periods of time, even when external reference signals are unavailable or unreliable. However, traditional inertial guidance sensors are too large, too expensive and too inflexible to be adapted to a handheld device.
Microelectronic mechanical sensors (MEMS) hold promise for providing functionality of similar to inertial guidance systems, while satisfying the form factor and production budget requirements for handheld devices. However, they are noisy, in an information theory sense, and operate in an algorithmically challenging environment. Human motion is algorithmically challenging because the position and orientation of the handheld device can vary rapidly, relative to overall movement of a person or vehicle conveying the handheld device. For instance, sensors in a smart phone will sense a pedestrian's stride and the pedestrian's gestures to operate the phone, such as viewing a text message or making a telephone call. Some observers have predicted that it will take another 3 to 5 years to develop practical algorithms that utilize MEMS data for dead reckoning over more than just a few seconds.
An opportunity arises to provide new and improved tools for use by developers and producers of smart phones, hand-held devices and other portable devices that deliver position data. In particular, this disclosure focuses on test tools.